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Tuning particle accelerators with safety constraints using Bayesian optimization

Published:2022-06-28 Issue:6 Volume:25 Page:
ISSN:2469-9888
Container-title:Physical Review Accelerators and Beams
language:en
Short-container-title:Phys. Rev. Accel. Beams

Author:

Kirschner Johannes^ORCID,Mutný Mojmir,Krause Andreas^ORCID,Coello de Portugal Jaime^ORCID,Hiller Nicole,Snuverink Jochem^ORCID

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

H2020 European Research Council

Horizon 2020 Framework Programme

Swiss Data Science Center

Publisher

American Physical Society (APS)

Subject

Surfaces and Interfaces,Physics and Astronomy (miscellaneous),Nuclear and High Energy Physics

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevAccelBeams.25.062802/fulltext

Reference49 articles.

1. Bayesian Approach to Global Optimization

2. A Simplex Method for Function Minimization

3. Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)

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1. Experimental Safe Extremum Seeking for Accelerators;IEEE Transactions on Control Systems Technology;2024-09

2. Machine-learning approach for operating electron beam at KEK electron/positron injector linac;Physical Review Accelerators and Beams;2024-08-26

3. Bayesian optimization algorithms for accelerator physics;Physical Review Accelerators and Beams;2024-08-06

4. Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning;Scientific Reports;2024-07-08

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